docstr: Log embeddings to tensorboard
docstr_tok: ["Log","embeddings","to","tensorboard"]
code: def log_embeddings(self):
      """
      Log embeddings to tensorboard
      """
      for name, emb in self.input_embeddings.items():
          labels = self.hparams.embedding_labels[name]
          self.logger.experiment.add_embedding(
              emb.weight.data.detach().cpu(),
              metadata=labels,
              tag=name,
              global_step=self.global_step,
          )
  
imports: from copy import copy
  from typing import Dict, List, Tuple, Union
  from matplotlib import pyplot
  import numpy
  import torch
  from torch import nn
  from torchmetrics import Metric
  from pytorch_forecasting.data import TimeSeriesDataSet
  from pytorch_forecasting.data.encoders import NaNLabelEncoder
  from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, MultiHorizonMetric, MultiLoss, QuantileLoss
  from pytorch_forecasting.models.base_model import BaseModelWithCovariates
  from pytorch_forecasting.models.nn import LSTM, MultiEmbedding
  from pytorch_forecasting.models.temporal_fusion_transformer.sub_modules import AddNorm, GateAddNorm, GatedLinearUnit, GatedResidualNetwork, InterpretableMultiHeadAttention, VariableSelectionNetwork
  from pytorch_forecasting.utils import create_mask, detach, integer_histogram, masked_op, padded_stack, to_list
label_desc: Use image node_id 14 for calling the TemporalFusionTransformer obj's underlying member method code with example usage: obj.log_embeddings() without return types